Accurate estimates of transpiration (T) remain particularly challenging in mixed C3-C4 ecosystems, where the relative dominance of species and their physiological characteristics shifts dynamically throughout the growing season. Some methods for evapotranspiration (ET) partitioning, such as Flux Variance Similarity (FVS), typically require an explicit specification of either C3 or C4 photosynthetic pathways to parameterize intercellular carbon dioxide concentrations (ci). Other methods, such as Modified Relaxed Eddy Accumulation (MREA), Conditional Eddy Covariance (CEC), Conditional Eddy Accumulation (CEA), and the FVS optimum approach, do not require prior ci approximations or designation of C3 and C4 vegetation. This study evaluated and inter-compared five ET partitioning methods: FVS, MREA, CEA, CEC, and a water-use efficiency-integrated modification of CEC (CECw) within two differently managed (grazing and hay harvest) C3-C4 mixed tallgrass prairies in Central Oklahoma, USA. Specifically, we investigated how partitioning outputs varied when using a static, year-round C4 parameterization compared to a seasonally dynamic C3-C4 framework. Results indicated that T:ET ratios from FVS methods using constant ci values or ci/ca ratios were similar across seasonal and annual scales, regardless of whether a year-round C4 or temporally dynamic C3-C4 parameterization was applied. During peak growth, MREA and CEC produced the highest T:ET ratios (0.93–0.95), whereas FVS and CEA yielded more moderate values (<0.75), and CECw provided the lowest estimates (0.56–0.62). Consequently, MREA and CEC estimated T 10–20% higher than the biophysically reasonable estimates from FVS and CEA. Methodological discrepancies were most pronounced during periods when either stomatal or non-stomatal fluxes dominated, with closer agreement occurring when these exchanges were nearly equivalent. Validation against lysimeter measurements showed that FVS and CECw optimum approaches performed best (r = 0.84–0.85; RMSE = 0.23–0.25 mm d−1), while other methods deviated substantially. As the first intercomparison of five partitioning methods in mixed C3-C4 tallgrass prairies, this study demonstrates that the choice of method significantly influences the resulting ecosystem water budget.

Breeding high yielding forages with good nutritive values is difficult in semiarid and arid locations of the world. The goal of this study was to identify guar [Cyamopsis tetragonoloba (L.) Taub.] breeding lines with potential use as forages for the US southern Great Plains using additive main effect and multiplicative interaction (AMMI) analysis and AMMI stability values (ASV). Twenty six breeding lines and four commercial cultivars were evaluated at two locations during 2 years. Although no genotype had an ASV of 1 (best) for grain yield, biomass production, and the seven forage nutritive value traits, three genotypes had lower ASV than or equal to the commercial controls when averaged across all traits. Among many individual traits, several genotypes performed better than the commercial cultivars. For example, breeding line 25 had good ASV for biomass and seed yield production, whereas breeding line 8 had good acid detergent fiber, relative forage quality, and total digestible nutrients. The results of this study showed that the guar breeding lines have adequate adaptability, variability, and stability to create future high yielding guar varieties with good forage nutritive values.

Tallgrass prairies are vital ecosystems that support regional biodiversity and play a crucial role in global carbon cycling. However, the management practices and disturbances they face can significantly alter their roles as carbon sinks or sources. Despite their importance, the carbon source-sink status of differently managed tallgrass prairies, especially under varying weather conditions, remains uncertain. This study utilized eddy covariance measurements of carbon dioxide (CO2) fluxes from four co-located tallgrass prairie pastures with different management regimes, including prescribed spring burns, intensive and rotational grazing, and haying. The primary objectives were to thoroughly evaluate the dynamics of net ecosystem CO2 exchange (NEE) and to investigate how diverse weather conditions affected carbon exchange across differently managed tallgrass prairies in central Oklahoma. The study period (2019-2024) experienced substantial variability in rainfall patterns. As expected, aboveground biomass and satellite-derived enhanced vegetation index (EVI) displayed distinct interannual variations. During the growing season (April-October, DOY similar to 100-300), pastures generally behaved as net carbon sinks, with NEE ranging from -33 to -478 g C m(-2). However, the magnitude and duration of the carbon sink and the overall annual carbon balance showed considerable interannual variations. Annual NEE ranged from 104 g C m(-2) (carbon source) to -362 g C m(-2) (carbon sink). Interannual variations in forage production, vegetation dynamics, and NEE were mainly influenced by rainfall variability, with growing season rainfall strongly correlating with peak biomass and EVI (R-2 = 0.67-0.71). Although management practices modulated these rainfall effects on carbon exchange, random forest analysis showed that EVI was the primary predictor of NEE across pastures, reflecting its capacity to integrate the combined effects of meteorological factors on carbon uptake. Our findings emphasize the need for adaptive management strategies tailored to local rainfall patterns and forecasts to optimize forage production, increase carbon sequestration, and strengthen the resilience of grassland ecosystems.

Understanding the annual dynamics of water use by rainfed and irrigated alfalfa (Medicago sativa L.) can support its sustainable management. Changes in evapotranspiration (ET) and plant growth patterns of alfalfa across years are scarce and are not well understood in the Southern Great Plains (SGP) of the United States (U.S.). The objectives of this study were to investigate the dynamics of eddy covariance (EC) measured ET (ETEC) and its controlling factors in rainfed and irrigated alfalfa and to compare ETEC dynamics with OpenET products that provide several established remote sensing-based ET model products (METRIC, PTJPL, SIMS, SSEBop, SEBAL, and DisALEXI) across the western U.S. The ETEC showed notable seasonal and interannual dynamics driven by meteorological conditions, vegetation dynamics, and water availability. Warmer and wetter conditions in April 2019 promoted initial alfalfa growth. Alfalfa’s water use (ET) mirrored its growth pattern throughout the year. Daily ETEC rates and cumulative ETEC at annual and seasonal scales were substantially lower than those reported for highly productive irrigated alfalfa in past studies. Satellite-derived enhanced vegetation index (EVI) and solar radiation (SR) explained 75% and 88% of variations in ETEC for all sites combined at 8-day and monthly scales, respectively. It indicates the potential of developing empirical models using readily available EVI and SR data to monitor alfalfa ET across large areas. When compared to ETEC, the performance of OpenET models varied widely, depending on field scenarios and criteria applied to model evaluations. SIMS and SSEBop demonstrated consistency and reliability in estimating ET for rainfed and irrigated alfalfa. DisALEXI and SEBAL performed poorly in irrigated alfalfa. METRIC and PTJPL exhibited poor performances under rainfed and irrigated conditions. By examining water use dynamics by alfalfa and the reliability of OpenET products, this study provides crucial information for effective water management practices for alfalfa.

Sorghum sudangrass (SSG), a vigorous hybrid C4 plant grown as forage or bioenergy crop during the summer has deeply distributed fibrous root system beneficial to dryland agriculture. Despite these advantages, the crop faces challenges from abiotic stressors, particularly during severe drought seasons. Thus, the goal of planting SSG as a forage is to generate ample biomass with minimal inputs, minimize toxicity, identify stress factors, and implement suitable management strategies with negligible environmental impact. This study focused on machine learning the intricate soil–plant-water-atmosphere relationships associated with abiotic stresses and agroenvironmental variables to enhance energy-efficiency and alleviate nitrogen (N) stress. A long-term simulation study involving SSG with four N treatments (spring-fallow and N fertilization, and three spring-time cover with 100

Precision agriculture, powered by the capabilities of hyperspectral remote sensing, has the potential to revolutionize crop management. This study investigates the impact of different ratios in training-testing data splits and corresponding sample sizes on predicting nutritional attributes (neutral detergent fiber [NDF], acid detergent fiber [ADF], crude protein [CP]) and biophysical/biochemical characteristics (N accumulation, biomass) of forage produced by three legumes. We used in-situ hyperspectral data and pseudo satellite-based data from a future sensor known as CHIME in conjunction with machine learning [ML] to examine attributes in soybean [Glycine max], tepary bean [Phaseolus acutifolius], mothbean [Vigna aconitifolia]. Different techniques of ML were applied, including weighted k-nearest neighbors (KKNN), support vector machines (SVM), and random forest (RF). Results indicate larger sample sizes lead to better model performance, and emphasized the importance of adequate data for training. The KKNN approach was most robust for in-situ data, while RF handles variations in train-test splits effectively for CHIME. The CHIME data regularly outperforms in-situ data with small datasets (105 P). Optimal ratios for train-test splits for peak model performance across all models of ML were 70:30-80:20. The study provides valuable insights into optimal sampling strategies and modeling techniques for using hyperspectral remote sensing as a tool in precision agriculture.

Researchers in hydrological sciences have developed agro-hydrological models to study water quantity and quality in small-scale watersheds. These models, however, often exhibit significant uncertainty in both parameters and response variables. The study aims to address limited research on the uncertainty range of runoffrelated parameters in watershed models, particularly those analyzing the impact of grazing operations. It also seeks to improve existing uncertainty analysis protocols because these protocols rely on parameter distributions, which are often difficult to determine. A generalized uncertainty analysis protocol that statistically considers multiple acceptable solutions from calibrated agro-hydrological models was developed. This approach employed a variant of the Agricultural Policy eXtender (APEX) model with an expanded grazing module called APEXgraze to perform uncertainty analysis of runoff and sediment-related parameters. Four small-scale watershed models were developed for calibration: a) native prairie, b) native prairie under grazing operations, c) cereals (winter wheat and one season of oats), and d) the same cereals under grazing operations in a semi-arid region of Oklahoma, United States. This work demonstrated that a simplified uncertainty analysis approach effectively captured the internal dynamics of hydrological processes within a statistically significant range of parameters. This observation was evidenced by a small range of water balance in both magnitude and percentage. The procedure also helped identify redundant parameters in sensitivity and uncertainty analyses. The proposed generalized uncertainty analysis protocol offers a reliable method for assessing hydrological models' internal dynamics and identifying critical parameters. This approach can enhance the accuracy of watershed models, particularly in regions with grazing operations.

The tallgrass prairie of the Great Plains is an ecologically and economically important grassland ecosystem in the United States. Prairies face significant challenges from weather variability (such as changing precipitation patterns, increased droughts, and heat waves) and management-related disturbances (such as prescribed burns, hay production, and grazing). This study examines the responses of tallgrass prairie to weather variability and management practices using data from the long-term, multi-factor "integrated Grassland-Livestock and Burning Experiment (iGLOBE)" in central Oklahoma. The experiment includes a cluster of eddy covariance (EC) systems across five native tallgrass prairies managed with different grazing, hay production, and burning regimes. The major objectives were to 1) quantify the variations in EC-measured evapotranspiration (ET) at different temporal scales across differently managed prairies under varying environmental conditions, and 2) combine remotely sensed vegetation indices with ET to assess their potential for monitoring and examining ecosystem responses to variable weather and management. Interannual variations in precipitation patterns during the study period (2019-2024) influenced vegetation dynamics, forage production, and ET. Temperature variability also played a crucial role in modifying the impact of precipitation, particularly during the early and late growing seasons. The observed ranges of maximum daily, growing season (April-October), and annual ET were 4.9-8.64 mm d-1 , 468-716 mm, and 546-861 mm, respectively, across pastures. Annual ET: precipitation ratios ranged from 0.67 in wet years to 1.15 in dry years. This study provides a ground-truth ET dataset across different weather and management scenarios, enabling validation of ET estimates from models and satellite-derived products for tallgrass prairies, even where direct ET measurements are unavailable. A strong agreement (R2 >= 0.70) between satellite-derived enhanced vegetation index (EVI) and EC-measured ET demonstrated the potential to combine these datasets for more precise quantification of how weather and management affect productivity and water use across native prairie landscapes. Published by Elsevier Inc. on behalf of The Society for Range Management.

Conservation management in dryland agriculture preserves water, improves soil health and yields. To comprehend the complex interactions of conservation management and environmental factors in a rainfed forage system of the US Great Plains, distinguish the superior influence of conservation over conventional management, and have a different perspective from simulation modeling, machine learning (ML) and artificial intelligence models were adapted in 2022. The variables in this study included ten years of daily recorded weather data and yield values simulated by the DSSAT model suite, considering four years of actual data on aboveground and belowground biomass, depth-wise carbon, water content, various physicochemical soil parameters, and management practices (Sarkar and Northup 2023). Two optimized ML models, Random Forest and AdaBoost, were found to perform better, when the algorithms of six ML models- namely Decision Tree, Random Forest, Bagging, Gradient Boosting, AdaBoost and XGBoost were tuned with different hyperparameters, validated and trained before predicting the biomass yields. Feature Importance plotting by these two models revealed the five most influencing similar variables, which were in different orders: average maximum temperature during daylight hours, total soil water, seasonal average minimum temperature, cumulative potential evapotranspiration and CO2. Hence, SHapley Additive exPlanation (SHAP) algorithm was adopted to dive into the database and clarify the interaction effects of management practices especially tillage and soil cover with different environmental variables. Interestingly, the SHAP model indicated soil cover as the 5th most important variable, followed by maximum temperature during daylight hours, cumulative potential evapotranspiration, seasonal minimum temperature and CO2. The interaction plotting of SHAP analysis also manifested that intensity of tillage and use of no soil cover could be detrimental. Considering the rising atmospheric CO2 levels and temperatures, along with depleting soil water, no-till practices with a springtime cover of grass peas or field peas and the addition of 100 % residue can be acclaimed for high water-use efficiency and increased aboveground biomass of rainfed sorghum sudangrass in drylands. We recommend using impeccable dataset, particularly from diverse agroenvironmental systems with various tillage practices and soil covers, before regional adoption. Additionally, exploring the impacts on diverse soil types is advisable before selecting a sustainable management strategy for precision agriculture.

The Southern Plains (SP) is one of 18 Long-Term Agroecosystem Research network sites that combine strategic research projects with common measurements across multiple agroecosystems. Projects at the SP site focus on the use of indicator measurements to aid in assessment of land and nutrient management's impact on soil health, water quality, carbon and water balances, and forage biomass-quality in diversified, adaptive crop-livestock systems designed to overcome shifts in natural resources and climate. The prevailing treatment is tilled winter wheat (Triticum aestivum L.) that is grazed, hayed, harvested for grain, or grazed and harvested for grain. The alternative treatment is year-round annual cover crop forage mixes for cattle (Bos taurus) production planted in fall and spring under conservation tillage management. The area is subject to variable weather and climatic shifts that reduce the potential to diversify forage crops and limit grazing in southern tall grass prairies and small grain systems. Incorporation of fertilized, rain-fed, annual cool and warm season mixtures of cover crops could fill forage gaps. The presence of year-round ground cover reduces sediment and nutrient loading to surface waters while enhancing soil health and water holding capacity. Tools to aid agricultural producers and land and water resource managers have been developed and implemented to determine how climate, topography, and varying conservation management practices alter hydrological structures, greenhouse gas emissions, water usage, and soil resources.

Recently, the Agricultural Policy Extender (APEX) model was enhanced with a grazing module, and the modified grazing database, APEXgraze, recommends sustainable livestock farming practices. This study developed a combinatorial deterministic approach to calibrate runoff-related parameters, assuming a normal probability distribution for each parameter. Using the calibrated APEXgraze model, the impact of grazing operations on native prairie and cropland planted with winter wheat and oats in central Oklahoma was assessed. The existing performance criteria produced four solutions with very close values for calibrating runoff at the farm outlet, exhibiting equifinality. The calibrated results showed that runoff representations had coefficients of determination and Nash–Sutcliffe efficiencies >0.6 in both watersheds, irrespective of grazing operations. Because of non-unique solutions, the key parameter settings revealed different metrics yielding different response variables. Based on the least objective function value, the behavior of watersheds under different management and grazing intensities was compared. Model simulations indicated significantly reduced water yield, deep percolation, sediment yield, phosphorus and nitrogen loadings, and plant temperature stress after imposing grazing, particularly in native prairies, as compared to croplands. Differences in response variables were attributed to the intensity of tillage and grazing activities. As expected, grazing reduced forage yields in native prairies and increased crop grain yields in cropland. The use of a combinatorial deterministic approach to calibrating parameters offers several new research benefits when developing farm management models and quantifying sensitive parameters and uncertainties that recommend optimal farm management strategies under different climate and management conditions.

This research established a generalized framework for sensitivity analysis of a set of parameters for a hydrological model. This method fine-tuned the calibrated parameters within arbitrary ranges and generated sets of model parameters and outcomes for deriving sensitivity indices. This study aimed to: a) improve the available frameworks for sensitivity analysis of the (agro) hydrological model; b) utilize previously calibrated parameters for APEXgraze – a recently upgraded Agricultural Policy/Environmental Extender (APEX) with enhanced grazing module – to investigate how runoff and sediment-related parameters are sensitive to model output or performance in watersheds with and without grazing operations; c) assess sensitivity indices to identify suitable strategies for sustainable management of agroecosystems. This framework was tested on a calibrated agro-hydrological APEXgraze model to investigate sensitive hydrological parameters under grazing on native prairie and cropland planted to winter wheat with one season of oats during summer fallow. Sensitive parameters were investigated in four ways: a) a percent-based one-to-one relationship, b) tests of two variance-based SOBOL and Fourier amplitude tests, and c) coefficients provided by standard regression. The study revealed different sensitive parameters in response to different approaches used in sensitivity analysis, though all used the same parameter sets and model outcomes. However, these indices did not reflect consistent results. Therefore, modelers should make informed judgments about the approach applied to sensitivity analysis, regardless of the results.

Tallgrass prairie is one of the major ecosystems in the Southern Great Plains of the United States of America (USA). We investigated the impact of diverse weather conditions on the vegetation dynamics obtained through satellite remote sensing and the dynamics of carbon dioxide (CO2) fluxes, evapotranspiration (ET), and ecosystem water use efficiency (EWUE) obtained through eddy covariance (EC) in a native tallgrass prairie pasture in central Oklahoma, USA. The study was conducted from 2019 to 2022, considering varying growing conditions. Daily peak net ecosystem CO2 exchange (NEE), gross primary production (GPP), and ET were −8.7 g C m−2, 15.1 g C m−2, and 5.9 mm, respectively. Dynamics of eddy fluxes aligned with the dynamics of vegetation, indicating the accuracy and reliability of satellite-based vegetation indices in tallgrass prairie. Both CO2 fluxes and ET rates showed little variation over the years during the non-growing season (November to March). However, eddy fluxes exhibited diverse patterns during growing seasons. During the regrowth phase after the hay harvest, the differences in eddy fluxes were particularly substantial due to large variations in late-season rainfall. Consequently, the strength and duration of carbon gain during growing seasons varied substantially by year (i.e., carbon sink for 6 mo in 2019 vs. 2 mo in 2022). The highly variable magnitude of EWUE over the years illustrates that EWUE is not a constant property of this prairie ecosystem. A greater reduction in GPP than ET during dry years led to reduced EWUE. Strong relationships between eddy fluxes and vegetation indices suggest that CO2 fluxes and ET can be estimated from satellite imagery alone for tallgrass prairie across large spatial scales. Overall, this study provides valuable insights into the carbon and water cycles of tallgrass prairie and the impacts of environmental drivers and disturbances on their function.

Winter wheat (Triticum aestivum L.) is an essential, high-quality forage used for grazing stocker cattle from fall to spring in the US Southern Great Plains (SGP). However, the lack of nutritious forages during summers limits grazing by stocker cattle. To fill this quality gap, a short season species capable of producing significant yield and quality of forage is necessary. A two-year experiment was conducted to evaluate the performance of three legumes: tepary bean (Phaseolus acutifolius A. Gray), mothbean [Vigna aconitifolia (Jacq.) Marechal], and soybean [Glycine max (L.) Merr.] as a control, at different harvest dates, in response to different row spacing (38 ​cm and 76 ​cm) and moisture levels (rainfed and irrigated). Results showed forage yield by all legumes planted at 38 ​cm spacing (4.5 and 3.9 ​Mg ​ha−1) was higher than at 76 ​cm spacing (3.4 and 2.4 ​Mg ​ha−1) in 2018 and 2019. Soybean was the most productive while mothbean had the highest relative feed value (RFV) in both 2018 and 2019 (160 and 118, respectively). Although soybean produced more forage, mothbean and tepary bean provided high quality forage in terms of neutral detergent fiber (NDF), acid detergent fiber (ADF), and in-vitro true digestibility (IVTD). The results indicate that no single legume species stands out as the unequivocal leader in delivering both high-quality and abundant forage. Consequently, the choice of which species to utilize should be tailored to the specific forage requirements and management goals. Future research should explore mothbean genotypes to identify cultivars with greater yield potential and develop agronomic practices that effectively utilize those cultivars.

Use of cover crops have been suggested to increase agricultural sustainability by providing multiple ecosystems services. Replacing summer fallow with drought tolerant legumes could serve both as a cover and a green source of N for winter wheat (Triticum aestivum L.) and help mitigate economic and environmental problems by increasing resilience of agroecosystems in the United States (US) Southern Great Plains (SGP). Field experiments were conducted in 2018 and 2019 at the USDA - ARS Oklahoma and Central Plains Agricultural Research Center near El Reno, OK. The overall objective of the study was to quantify the potential of two summer legumes, tepary bean [Phaseolus acutifolius (A.) Gray] and moth bean [Vigna aconitifolia (Jacq.) Marechal], as green sources of N for winter wheat production in comparison with soybean [Glycine max (L.) Merr.]. The PROC MIXED procedure of the SAS statistical software was used to analyze biomass production by legumes, their impacts on soil water, soil N and yield of winter wheat, and N transfer from green N biomass to following winter wheat. Green N crops did not differ in biomass production, but soybean provided 22 % and 40 % more N than tepary bean and moth bean, respectively. Soil water was reduced under all green N crops than summer fallow at termination of green N crops and wheat planting. Biomass of green N crops increased N content in soils at wheat planting. Grain yield of winter wheat was significantly higher under tepary bean (5295 kg ha−1) than soybean (4300 kg ha−1), moth bean (4560 kg ha−1) and the control treatment (4323 kg ha−1). Nitrogen recovery in wheat biomass was greater under moth bean and tepary bean compared to soybean. Both moth bean and tepary bean could serve as green sources of N for winter wheat in the US SGP.

The annual dynamics of carbon dioxide (CO2) fluxes for irrigated and rainfed alfalfa (Medicago sativa L.) in the Southern Great Plains of the United States of America (USA) under different watering regimes are not yet fully understood. The main objective of this study was to examine the dynamics of eddy covariance (EC) measured CO2 fluxes in relation to various biophysical factors and hay harvests for irrigated and rainfed alfalfa in central Oklahoma, USA. The study also aimed to investigate the relationship between CO2 fluxes and satellite-derived enhanced vegetation index (EVI) at different spatiotemporal scales and to assess the temporal variability in CO2 fluxes and EVI to variable growing conditions and hay harvests. The cumulative hay yields were 7.15 t ha(-1) (two harvests in 2019) in the rainfed field and similar to 9 t ha(-1) (4-5 harvests in 2020 and 2021) in the irrigated field. Having sufficient rainfall during April and May was crucial to achieve economically feasible yields of alfalfa during the first harvest in May. The availability of water strongly regulated the potential for regrowth and carbon uptake of alfalfa following harvesting. The alfalfa fields were near carbon neutral or a small carbon source from January to mid-March and carbon sink after the initiation of vegetative growth in mid-March. The alfalfa fields were strong carbon sinks (cumulative annual net ecosystem CO2 exchange, NEE, up to -578 g C m(-2) in irrigated field) on an annual scale. When accounting for the loss of carbon due to the removal of hay from the fields, the carbon balance of the alfalfa fields varied from small carbon sinks to small carbon sources, depending on the amount of hay harvested annually and the growing conditions. In general, the temporal patterns of CO2 fluxes and EVI were similar in relation to growing conditions and hay harvests. However, some discrepancies and time lags were observed due to the coarse spatiotemporal resolution of the EVI products. Thus, it is essential to integrate two or more satellite products with different temporal and spatial resolutions to accurately monitor the frequent and varying sizes of hay harvests and vegetation regrowth after harvesting, and to simulate continuous time-series CO2 fluxes.

Dual use crops are becoming popular as resources like land and water become scarcer. Annual peanuts are an important food crop grown worldwide that can also be used as forage or hay. Replicated trials evaluated the late season (>= 15 weeks after planting) forage accumulations and nutritive values (acid detergent fiber, carbon content, crude protein, invitro true digestibility, neutral detergent fiber, nitrogen content, and relative feed value) of four market types near Clovis, NM and El Reno, OK in 2019 and 2020. Statistical analyses (p <= .05) showed all traits were affected by the interaction between market type and growing environment. Overall, forage accumulations for all market types ranged from 2.7 to 6.5 Mg ha(-1) with relative feed values of 107-155. The location average across sites was 4.45 Mg ha(-1) with relative feed value of 132. The Virginia market types produced large amounts of biomass. However, the Valencia market type generated the greatest nutritive values. This study indicates harvesting late-season biomass of peanuts for hay may be an option that allows producers to generate more income from limited land and water resources. However, both market type and environmental factors, such as rainfall/irrigation, play important roles in production and forage nutritive values of peanut forage.

Conserving soil moisture is an important safeguard for drought resiliency in crop production for the U.S. Southern Great Plains. Conservation tillage and cover crops are tools that can provide cover and protect soils and conserve moisture. We assessed variations in simulated water-stress for sorghum-sudangrass (Sorghum bicolor (L.) Moench x Sorghum sudanense (P.) Stapf.) grown for forage, and examined impacts generated by tillage systems (no-till, NT and conventional, CT) and three spring crops grown for green manures (oat, field pea, grass pea) compared to a control (spring fallow and 60 kg inorganic nitrogen ha(-1)). We calibrated and validated CERES-Sorghum model before simulating the biomass and agroecosystem functions (crop genetics and management, and soil). Seasonal analysis method was adopted to predict biomass and other water parameters during 2006 to 2015 and available soil water in two contrasting growing seasons (2012, dry; 2015, wet) in central Oklahoma, USA. We modified genotype-specific files and parameters of the 'Residue' file to simulate change in available moisture by soil layer. Sorghum-sudangrass produced slightly higher amounts of biomass under NT across years and green-manure crops. Weather variables significantly influenced sorghum-sudangrass during growing seasons. Green manures increased (P < 0.05) biomass production (229 to 328 kg ha(-1) yr(-1)), irrespective of tillage system; 63% of production was explained by form of tillage. Water stress on production was affected by growing seasons but not by nitrogen treatments. More water was available in subsurface soils (15-30 cm) across years under CT and was more uniform across treatments. Water availability under NT differed among years and varied by treatments within years; oat green-manure crops reduced available soil-moisture. Heatmap analyses showed strong negative effects on soil water is from cumulative evapotranspiration, which had strong positive relationships with maximum temperatures during daylight hours and solar radiations. Management with the combination of green manure and NT or CT were found as better practice to deal with water-stress and produce better than CT-60N. Overall, green-manure crops were an effective strategy for conserving soil moisture under CT, and NT during wetter growing seasons, and could be beneficial in conserving soil water in sorghum-sudangrass systems.

Tepary bean (Phaseolus acutifolius A. Gray) is an underutilized drought tolerant annual legume, originating from the Sonoran Desert, that may be a beneficial forage/hay for beef cattle in the Southern Great Plains of the US (SGP). The SGP has erratic rainfall and periods of intermittent drought exacerbated by high summer temperatures. In 2020 and 2021, a split-plot design was used to evaluate 13 genotypes of tepary bean and a forage soybean (control) at El Reno, OK, USA to compare production of plant biomass and forage nutritive value parameters under seven harvest regimes. Genotypes were used as the main plot and cutting management as the sub-plot. Biomass production of all tepary bean genotypes equaled that of soybean (p > 0.05), while several genotypes had superior forage nutritive value traits (p ≤ 0.05). Overall, a 15-cm cutting height and 30-day harvest interval produced the best overall product (average dry biomass of 5.8 Mg ha−1 with average relative feed values (RFV) of 165). Although all harvest regimes reduced total seasonal biomass, forage nutritive value increased. However, the tradeoff between forage production and nutritive value may be unacceptable to most producers. Further agronomic and breeding research is needed to encourage producers to grow tepary bean as a forage/hay in the SGP.